Photography is an ever-advancing technology: pixels are getting more densely packed, colors are going deeper, and light sensitivity is going higher. But the camera standard dynamic range largely remains less than the full brightness range of a natural scene. In the days of film cameras people have used the multiexposure high dynamic range imaging to capture the full dynamic range although only limited for static scenes. However, with digital photography, the exposure index can be spatially multiplexed on a sensor, which adapts the essence of the multi-exposure technique for single-image high dynamic range imaging. A sophisticated postprocessing is required; and existing techniques either lose final image resolution or take a long time to run. In this paper, we propose a novel method for this postprocessing: we use a robust global optimization method and a fast edge-preserving interpolation technique for full resolution exposure-multiplexed high dynamic range imaging.
Modern digital cameras have very limited dynamic range, which makes them unable to capture the full range of illumination in natural scenes. Since this prevents them from accurately photographing visible detail, researchers have spent the last two decades developing algorithms for high-dynamic range (HDR) imaging which can capture a wider range of illumination and therefore allow us to reconstruct richer images of natural scenes. The most practical of these methods are stack-based approaches which take a set of images at different exposure levels and then merge them together to form the final HDR result. However, these algorithms produce ghost-like artifacts when the scene has motion or the camera is not perfectly static. In this paper, we present an overview of state-of-the-art deghosting algorithms for stack-based HDR imaging and discuss some of the tradeoffs of each.
To reproduce the appearance of real world scenes, a number of color appearance models have been proposed thanks to adapted psycho-visual experiments. Most of them were designed and intended for a limited dynamic range, or address only dynamic range compression applications. However, given the increasing availability of displays with higher luminance and contrast ranges, a detailed analysis of appearance attributes is also necessary for dynamic range expansion scenarios. In this study, we propose a psycho-visual experimental setup, designed by adapting and combining the adjustment and partition scaling methodologies, which we employ for measuring perceptual colorfulness of color patches with different levels of lightness, chroma and hue. The proposed setup reduces the complexity and increases the efficiency relative to previous experimental setups and allows both expert and non-expert participants to be included. From the collected data, a modified color space is obtained and a new saturation model for dynamic range compression and expansion is derived for high dynamic range imaging.